{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import talib as ta\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "pro = ts.pro_api('0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-18</th>\n",
       "      <td>MA2201.ZCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2338.0</td>\n",
       "      <td>2309.0</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>2309.0</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>82.02</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-19</th>\n",
       "      <td>MA2201.ZCE</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>2343.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2377.0</td>\n",
       "      <td>2350.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>94.65</td>\n",
       "      <td>45.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-20</th>\n",
       "      <td>MA2201.ZCE</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>2349.0</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>2335.0</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>110.66</td>\n",
       "      <td>55.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-21</th>\n",
       "      <td>MA2201.ZCE</td>\n",
       "      <td>2337.0</td>\n",
       "      <td>2354.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>2362.0</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>2344.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>93.78</td>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-22</th>\n",
       "      <td>MA2201.ZCE</td>\n",
       "      <td>2352.0</td>\n",
       "      <td>2344.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>2351.0</td>\n",
       "      <td>2369.0</td>\n",
       "      <td>2361.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.28</td>\n",
       "      <td>83.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ts_code  pre_close  pre_settle    open    high     low   close  \\\n",
       "trade_date                                                                      \n",
       "2021-01-18  MA2201.ZCE        NaN      2338.0  2309.0  2397.0  2309.0  2357.0   \n",
       "2021-01-19  MA2201.ZCE     2357.0      2343.0  2358.0  2377.0  2350.0  2358.0   \n",
       "2021-01-20  MA2201.ZCE     2358.0      2366.0  2349.0  2375.0  2335.0  2337.0   \n",
       "2021-01-21  MA2201.ZCE     2337.0      2354.0  2336.0  2362.0  2333.0  2352.0   \n",
       "2021-01-22  MA2201.ZCE     2352.0      2344.0  2370.0  2370.0  2351.0  2369.0   \n",
       "\n",
       "            settle  change1  change2   vol  amount    oi  oi_chg  \n",
       "trade_date                                                        \n",
       "2021-01-18  2343.0     19.0      5.0  35.0   82.02  22.0     NaN  \n",
       "2021-01-19  2366.0     15.0     23.0  40.0   94.65  45.0    23.0  \n",
       "2021-01-20  2354.0    -29.0    -12.0  47.0  110.66  55.0    10.0  \n",
       "2021-01-21  2344.0     -2.0    -10.0  40.0   93.78  70.0    15.0  \n",
       "2021-01-22  2361.0     25.0     17.0  34.0   80.28  83.0    13.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tscode = 'ma2201.ZCE'\n",
    "df = pro.fut_daily(ts_code=tscode, start_date='20180101', end_date='20211125')\n",
    "df['trade_date'] = df['trade_date'].apply(lambda x: datetime.strptime(x,'%Y%m%d'))\n",
    "df = df.set_index('trade_date')\n",
    "df = df.sort_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>CUL.SHF</td>\n",
       "      <td>55150.0</td>\n",
       "      <td>55430.0</td>\n",
       "      <td>54430.0</td>\n",
       "      <td>55420.0</td>\n",
       "      <td>54430.0</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>54920.0</td>\n",
       "      <td>-330.0</td>\n",
       "      <td>-510.0</td>\n",
       "      <td>22504.0</td>\n",
       "      <td>617965.10</td>\n",
       "      <td>46860.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>CUL.SHF</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>54920.0</td>\n",
       "      <td>54950.0</td>\n",
       "      <td>55080.0</td>\n",
       "      <td>54570.0</td>\n",
       "      <td>54680.0</td>\n",
       "      <td>54710.0</td>\n",
       "      <td>-240.0</td>\n",
       "      <td>-210.0</td>\n",
       "      <td>21610.0</td>\n",
       "      <td>591168.65</td>\n",
       "      <td>43370.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>CUL.SHF</td>\n",
       "      <td>54680.0</td>\n",
       "      <td>54710.0</td>\n",
       "      <td>54600.0</td>\n",
       "      <td>55070.0</td>\n",
       "      <td>54430.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>54730.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22210.0</td>\n",
       "      <td>607846.90</td>\n",
       "      <td>41160.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>CUL.SHF</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>54730.0</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>55100.0</td>\n",
       "      <td>54540.0</td>\n",
       "      <td>54690.0</td>\n",
       "      <td>54720.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>21660.0</td>\n",
       "      <td>592705.05</td>\n",
       "      <td>37830.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>CUL.SHF</td>\n",
       "      <td>54690.0</td>\n",
       "      <td>54720.0</td>\n",
       "      <td>54410.0</td>\n",
       "      <td>54530.0</td>\n",
       "      <td>54140.0</td>\n",
       "      <td>54360.0</td>\n",
       "      <td>54280.0</td>\n",
       "      <td>-360.0</td>\n",
       "      <td>-440.0</td>\n",
       "      <td>24050.0</td>\n",
       "      <td>652750.60</td>\n",
       "      <td>33320.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ts_code  pre_close  pre_settle     open     high      low  \\\n",
       "trade_date                                                              \n",
       "2018-01-02  CUL.SHF    55150.0     55430.0  54430.0  55420.0  54430.0   \n",
       "2018-01-03  CUL.SHF    55100.0     54920.0  54950.0  55080.0  54570.0   \n",
       "2018-01-04  CUL.SHF    54680.0     54710.0  54600.0  55070.0  54430.0   \n",
       "2018-01-05  CUL.SHF    55000.0     54730.0  55100.0  55100.0  54540.0   \n",
       "2018-01-08  CUL.SHF    54690.0     54720.0  54410.0  54530.0  54140.0   \n",
       "\n",
       "              close   settle  change1  change2      vol     amount       oi  \\\n",
       "trade_date                                                                    \n",
       "2018-01-02  55100.0  54920.0   -330.0   -510.0  22504.0  617965.10  46860.0   \n",
       "2018-01-03  54680.0  54710.0   -240.0   -210.0  21610.0  591168.65  43370.0   \n",
       "2018-01-04  55000.0  54730.0    290.0     20.0  22210.0  607846.90  41160.0   \n",
       "2018-01-05  54690.0  54720.0    -40.0    -10.0  21660.0  592705.05  37830.0   \n",
       "2018-01-08  54360.0  54280.0   -360.0   -440.0  24050.0  652750.60  33320.0   \n",
       "\n",
       "           oi_chg  \n",
       "trade_date         \n",
       "2018-01-02   None  \n",
       "2018-01-03   None  \n",
       "2018-01-04   None  \n",
       "2018-01-05   None  \n",
       "2018-01-08   None  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tscode = 'CUL.SHF'\n",
    "df = pro.fut_daily(ts_code=tscode, start_date='20180101', end_date='20221125')\n",
    "df['trade_date'] = df['trade_date'].apply(lambda x: datetime.strptime(x,'%Y%m%d'))\n",
    "df = df.set_index('trade_date')\n",
    "df = df.sort_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>symbol</th>\n",
       "      <th>name</th>\n",
       "      <th>list_date</th>\n",
       "      <th>delist_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JD1907.DCE</td>\n",
       "      <td>JD1907</td>\n",
       "      <td>鸡蛋1907</td>\n",
       "      <td>20180727</td>\n",
       "      <td>20190726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CS1505.DCE</td>\n",
       "      <td>CS1505</td>\n",
       "      <td>玉米淀粉1505</td>\n",
       "      <td>20141219</td>\n",
       "      <td>20150515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1003.DCE</td>\n",
       "      <td>C1003</td>\n",
       "      <td>玉米1003</td>\n",
       "      <td>20090316</td>\n",
       "      <td>20100312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M1708.DCE</td>\n",
       "      <td>M1708</td>\n",
       "      <td>豆粕1708</td>\n",
       "      <td>20160815</td>\n",
       "      <td>20170814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C1301.DCE</td>\n",
       "      <td>C1301</td>\n",
       "      <td>玉米1301</td>\n",
       "      <td>20120118</td>\n",
       "      <td>20130117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2208</th>\n",
       "      <td>Y2207.DCE</td>\n",
       "      <td>Y2207</td>\n",
       "      <td>豆油2207</td>\n",
       "      <td>20210715</td>\n",
       "      <td>20220714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2209</th>\n",
       "      <td>Y1403.DCE</td>\n",
       "      <td>Y1403</td>\n",
       "      <td>豆油1403</td>\n",
       "      <td>20130315</td>\n",
       "      <td>20140314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2210</th>\n",
       "      <td>M1401.DCE</td>\n",
       "      <td>M1401</td>\n",
       "      <td>豆粕1401</td>\n",
       "      <td>20130118</td>\n",
       "      <td>20140115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2211</th>\n",
       "      <td>JM1611.DCE</td>\n",
       "      <td>JM1611</td>\n",
       "      <td>焦煤1611</td>\n",
       "      <td>20151116</td>\n",
       "      <td>20161114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2212</th>\n",
       "      <td>B1007.DCE</td>\n",
       "      <td>B1007</td>\n",
       "      <td>豆二1007</td>\n",
       "      <td>20090715</td>\n",
       "      <td>20100714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2213 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ts_code  symbol      name list_date delist_date\n",
       "0     JD1907.DCE  JD1907    鸡蛋1907  20180727    20190726\n",
       "1     CS1505.DCE  CS1505  玉米淀粉1505  20141219    20150515\n",
       "2      C1003.DCE   C1003    玉米1003  20090316    20100312\n",
       "3      M1708.DCE   M1708    豆粕1708  20160815    20170814\n",
       "4      C1301.DCE   C1301    玉米1301  20120118    20130117\n",
       "...          ...     ...       ...       ...         ...\n",
       "2208   Y2207.DCE   Y2207    豆油2207  20210715    20220714\n",
       "2209   Y1403.DCE   Y1403    豆油1403  20130315    20140314\n",
       "2210   M1401.DCE   M1401    豆粕1401  20130118    20140115\n",
       "2211  JM1611.DCE  JM1611    焦煤1611  20151116    20161114\n",
       "2212   B1007.DCE   B1007    豆二1007  20090715    20100714\n",
       "\n",
       "[2213 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pro.fut_basic(exchange='DCE', fut_type='1', fields='ts_code,symbol,name,list_date,delist_date')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pro.fut_mapping(ts_code='TF.CFX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>mapping_ts_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20220314</td>\n",
       "      <td>TF2206.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20220311</td>\n",
       "      <td>TF2206.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20220310</td>\n",
       "      <td>TF2206.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20220309</td>\n",
       "      <td>TF2206.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20220308</td>\n",
       "      <td>TF2206.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20130912</td>\n",
       "      <td>TF1312.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20130911</td>\n",
       "      <td>TF1312.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20130910</td>\n",
       "      <td>TF1312.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20130909</td>\n",
       "      <td>TF1312.CFX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>TF.CFX</td>\n",
       "      <td>20130906</td>\n",
       "      <td>TF1312.CFX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2071 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date mapping_ts_code\n",
       "0     TF.CFX   20220314      TF2206.CFX\n",
       "1     TF.CFX   20220311      TF2206.CFX\n",
       "2     TF.CFX   20220310      TF2206.CFX\n",
       "3     TF.CFX   20220309      TF2206.CFX\n",
       "4     TF.CFX   20220308      TF2206.CFX\n",
       "...      ...        ...             ...\n",
       "2066  TF.CFX   20130912      TF1312.CFX\n",
       "2067  TF.CFX   20130911      TF1312.CFX\n",
       "2068  TF.CFX   20130910      TF1312.CFX\n",
       "2069  TF.CFX   20130909      TF1312.CFX\n",
       "2070  TF.CFX   20130906      TF1312.CFX\n",
       "\n",
       "[2071 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pro.fut_daily(trade_date='20181113', exchange='DCE', fields='ts_code,trade_date,pre_close,pre_settle,open,high,low,close,settle,vol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curve(df_ma_sma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>3493.0</td>\n",
       "      <td>3502.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>3452.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>3476.0</td>\n",
       "      <td>121434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1811.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>3448.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3430.0</td>\n",
       "      <td>3430.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A1901.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>3493.0</td>\n",
       "      <td>3502.0</td>\n",
       "      <td>3509.0</td>\n",
       "      <td>3452.0</td>\n",
       "      <td>3471.0</td>\n",
       "      <td>3476.0</td>\n",
       "      <td>121434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A1903.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>3578.0</td>\n",
       "      <td>3579.0</td>\n",
       "      <td>3529.0</td>\n",
       "      <td>3529.0</td>\n",
       "      <td>3554.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A1905.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>3715.0</td>\n",
       "      <td>3701.0</td>\n",
       "      <td>3718.0</td>\n",
       "      <td>3732.0</td>\n",
       "      <td>3680.0</td>\n",
       "      <td>3695.0</td>\n",
       "      <td>3702.0</td>\n",
       "      <td>17726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>Y1905.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>5554.0</td>\n",
       "      <td>5526.0</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>5514.0</td>\n",
       "      <td>5536.0</td>\n",
       "      <td>5538.0</td>\n",
       "      <td>64596.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Y1907.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>5586.0</td>\n",
       "      <td>5614.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5614.0</td>\n",
       "      <td>5614.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Y1908.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5698.0</td>\n",
       "      <td>5698.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Y1909.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>5644.0</td>\n",
       "      <td>5624.0</td>\n",
       "      <td>5632.0</td>\n",
       "      <td>5654.0</td>\n",
       "      <td>5620.0</td>\n",
       "      <td>5638.0</td>\n",
       "      <td>5636.0</td>\n",
       "      <td>1538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>YL.DCE</td>\n",
       "      <td>20181113</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>5446.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts_code trade_date  pre_close  pre_settle    open    high     low  \\\n",
       "0        A.DCE   20181113     3504.0      3493.0  3502.0  3509.0  3452.0   \n",
       "1    A1811.DCE   20181113     3460.0      3448.0     NaN     NaN     NaN   \n",
       "2    A1901.DCE   20181113     3504.0      3493.0  3502.0  3509.0  3452.0   \n",
       "3    A1903.DCE   20181113     3498.0      3498.0  3578.0  3579.0  3529.0   \n",
       "4    A1905.DCE   20181113     3715.0      3701.0  3718.0  3732.0  3680.0   \n",
       "..         ...        ...        ...         ...     ...     ...     ...   \n",
       "195  Y1905.DCE   20181113     5554.0      5526.0  5550.0  5558.0  5514.0   \n",
       "196  Y1907.DCE   20181113     5586.0      5614.0     NaN     NaN     NaN   \n",
       "197  Y1908.DCE   20181113     5686.0      5686.0     NaN     NaN     NaN   \n",
       "198  Y1909.DCE   20181113     5644.0      5624.0  5632.0  5654.0  5620.0   \n",
       "199     YL.DCE   20181113     5446.0      5446.0     NaN     NaN     NaN   \n",
       "\n",
       "      close  settle       vol  \n",
       "0    3471.0  3476.0  121434.0  \n",
       "1    3430.0  3430.0       0.0  \n",
       "2    3471.0  3476.0  121434.0  \n",
       "3    3529.0  3554.0      28.0  \n",
       "4    3695.0  3702.0   17726.0  \n",
       "..      ...     ...       ...  \n",
       "195  5536.0  5538.0   64596.0  \n",
       "196  5614.0  5614.0       0.0  \n",
       "197  5698.0  5698.0       0.0  \n",
       "198  5638.0  5636.0    1538.0  \n",
       "199  5446.0  5446.0       0.0  \n",
       "\n",
       "[200 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5289.0</td>\n",
       "      <td>5180.0</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>250381.0</td>\n",
       "      <td>1310197.68</td>\n",
       "      <td>185819.0</td>\n",
       "      <td>-5089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-23</td>\n",
       "      <td>5134.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>5166.0</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>5116.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>263255.0</td>\n",
       "      <td>1363265.42</td>\n",
       "      <td>190908.0</td>\n",
       "      <td>-15226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-22</td>\n",
       "      <td>5091.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5173.0</td>\n",
       "      <td>4961.0</td>\n",
       "      <td>5134.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>331751.0</td>\n",
       "      <td>1688453.37</td>\n",
       "      <td>206134.0</td>\n",
       "      <td>-11459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-19</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5042.0</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5136.0</td>\n",
       "      <td>4883.0</td>\n",
       "      <td>5091.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>396160.0</td>\n",
       "      <td>1977926.74</td>\n",
       "      <td>217593.0</td>\n",
       "      <td>2974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>5116.0</td>\n",
       "      <td>5101.0</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>5128.0</td>\n",
       "      <td>4952.0</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5042.0</td>\n",
       "      <td>-136.0</td>\n",
       "      <td>-59.0</td>\n",
       "      <td>386237.0</td>\n",
       "      <td>1947607.52</td>\n",
       "      <td>214619.0</td>\n",
       "      <td>10862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code trade_date  pre_close  pre_settle    open    high     low  \\\n",
       "0  EG2201.DCE 2021-11-24     5200.0      5178.0  5200.0  5289.0  5180.0   \n",
       "1  EG2201.DCE 2021-11-23     5134.0      5089.0  5166.0  5248.0  5116.0   \n",
       "2  EG2201.DCE 2021-11-22     5091.0      4992.0  5000.0  5173.0  4961.0   \n",
       "3  EG2201.DCE 2021-11-19     4965.0      5042.0  4965.0  5136.0  4883.0   \n",
       "4  EG2201.DCE 2021-11-18     5116.0      5101.0  5110.0  5128.0  4952.0   \n",
       "\n",
       "    close  settle  change1  change2       vol      amount        oi   oi_chg  \n",
       "0  5216.0  5232.0     38.0     54.0  250381.0  1310197.68  185819.0  -5089.0  \n",
       "1  5200.0  5178.0    111.0     89.0  263255.0  1363265.42  190908.0 -15226.0  \n",
       "2  5134.0  5089.0    142.0     97.0  331751.0  1688453.37  206134.0 -11459.0  \n",
       "3  5091.0  4992.0     49.0    -50.0  396160.0  1977926.74  217593.0   2974.0  \n",
       "4  4965.0  5042.0   -136.0    -59.0  386237.0  1947607.52  214619.0  10862.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5216.0\n",
       "1      5200.0\n",
       "2      5134.0\n",
       "3      5091.0\n",
       "4      4965.0\n",
       "        ...  \n",
       "194    4605.0\n",
       "195    4615.0\n",
       "196    4462.0\n",
       "197    4437.0\n",
       "198    4360.0\n",
       "Name: close, Length: 199, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def get_df_smas(df, lenthlist):\n",
    "    df_temp = pd.DataFrame(df.close)\n",
    "    df_temp.index = df.index\n",
    "    for i in lenthlist:\n",
    "        df_temp['sma_'+ str(i)] = ta.SMA(df['close'], i)\n",
    "    \n",
    "    return df_temp\n",
    "\n",
    "def plot_curve(plot_dict):\n",
    "    \"\"\"\n",
    "    plot the net asset value curve\n",
    "    :param plot_dict: type--dict, {label name (str): time series data (pd.Series)}\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    fig, ax = plt.subplots(figsize=(28, 8))\n",
    "    fig.patch.set_facecolor('white')\n",
    "    transparency_level = 1.\n",
    "    \n",
    "    for _ts_label, _ts_curve in plot_dict.items():\n",
    "        _ts_curve.plot(ax=ax, lw=1.5, label=_ts_label, alpha=transparency_level)\n",
    "    \n",
    "    ax.legend(loc='upper left', shadow=True)    \n",
    "\n",
    "    plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma_sma = get_df_smas(df, [5, 12])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_ma_sma = df_ma_sma.dropna()\n",
    "# !python -m pip install --upgrade pip\n",
    "!pip install -U numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_10</th>\n",
       "      <th>sma_20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5586.0</td>\n",
       "      <td>5641.8</td>\n",
       "      <td>5576.1</td>\n",
       "      <td>5384.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6071.0</td>\n",
       "      <td>5714.0</td>\n",
       "      <td>5642.2</td>\n",
       "      <td>5428.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6257.0</td>\n",
       "      <td>5833.6</td>\n",
       "      <td>5720.1</td>\n",
       "      <td>5484.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6303.0</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>5809.3</td>\n",
       "      <td>5545.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6507.0</td>\n",
       "      <td>6144.8</td>\n",
       "      <td>5903.7</td>\n",
       "      <td>5622.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     close   sma_5  sma_10   sma_20\n",
       "19  5586.0  5641.8  5576.1  5384.80\n",
       "20  6071.0  5714.0  5642.2  5428.35\n",
       "21  6257.0  5833.6  5720.1  5484.50\n",
       "22  6303.0  5998.0  5809.3  5545.10\n",
       "23  6507.0  6144.8  5903.7  5622.20"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ma_sma.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-11-24</th>\n",
       "      <td>5216.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-23</th>\n",
       "      <td>5200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-22</th>\n",
       "      <td>5134.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-19</th>\n",
       "      <td>5091.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-18</th>\n",
       "      <td>4965.0</td>\n",
       "      <td>5121.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>4605.0</td>\n",
       "      <td>4610.0</td>\n",
       "      <td>4689.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>4615.0</td>\n",
       "      <td>4607.0</td>\n",
       "      <td>4666.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>4462.0</td>\n",
       "      <td>4589.4</td>\n",
       "      <td>4632.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>4437.0</td>\n",
       "      <td>4547.4</td>\n",
       "      <td>4595.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>4360.0</td>\n",
       "      <td>4495.8</td>\n",
       "      <td>4568.916667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>199 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             close   sma_5       sma_12\n",
       "trade_date                             \n",
       "2021-11-24  5216.0     NaN          NaN\n",
       "2021-11-23  5200.0     NaN          NaN\n",
       "2021-11-22  5134.0     NaN          NaN\n",
       "2021-11-19  5091.0     NaN          NaN\n",
       "2021-11-18  4965.0  5121.2          NaN\n",
       "...            ...     ...          ...\n",
       "2021-02-02  4605.0  4610.0  4689.750000\n",
       "2021-02-01  4615.0  4607.0  4666.083333\n",
       "2021-01-29  4462.0  4589.4  4632.166667\n",
       "2021-01-28  4437.0  4547.4  4595.416667\n",
       "2021-01-27  4360.0  4495.8  4568.916667\n",
       "\n",
       "[199 rows x 3 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ma_sma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['close'].plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ma_sma = get_df_smas(df, [5, 12])\n",
    "plot_curve(df_ma_sma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'str' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:163\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m    162\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 163\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:239\u001b[0m, in \u001b[0;36mevaluate\u001b[1;34m(op, a, b, use_numexpr)\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m use_numexpr:\n\u001b[0;32m    238\u001b[0m         \u001b[38;5;66;03m# error: \"None\" not callable\u001b[39;00m\n\u001b[1;32m--> 239\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_evaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_str\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _evaluate_standard(op, op_str, a, b)\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:69\u001b[0m, in \u001b[0;36m_evaluate_standard\u001b[1;34m(op, op_str, a, b)\u001b[0m\n\u001b[0;32m     68\u001b[0m     _store_test_result(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m---> 69\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'float'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpct_change\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\generic.py:10376\u001b[0m, in \u001b[0;36mNDFrame.pct_change\u001b[1;34m(self, periods, fill_method, limit, freq, **kwargs)\u001b[0m\n\u001b[0;32m  10374\u001b[0m shifted \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mshift(periods\u001b[38;5;241m=\u001b[39mperiods, freq\u001b[38;5;241m=\u001b[39mfreq, axis\u001b[38;5;241m=\u001b[39maxis, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m  10375\u001b[0m \u001b[38;5;66;03m# Unsupported left operand type for / (\"NDFrameT\")\u001b[39;00m\n\u001b[1;32m> 10376\u001b[0m rs \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mshifted\u001b[49m \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m  \u001b[38;5;66;03m# type: ignore[operator]\u001b[39;00m\n\u001b[0;32m  10377\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m freq \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m  10378\u001b[0m     \u001b[38;5;66;03m# Shift method is implemented differently when freq is not None\u001b[39;00m\n\u001b[0;32m  10379\u001b[0m     \u001b[38;5;66;03m# We want to restore the original index\u001b[39;00m\n\u001b[0;32m  10380\u001b[0m     rs \u001b[38;5;241m=\u001b[39m rs\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m~\u001b[39mrs\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mduplicated()]\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\ops\\common.py:70\u001b[0m, in \u001b[0;36m_unpack_zerodim_and_defer.<locals>.new_method\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m     66\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[0;32m     68\u001b[0m other \u001b[38;5;241m=\u001b[39m item_from_zerodim(other)\n\u001b[1;32m---> 70\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\arraylike.py:124\u001b[0m, in \u001b[0;36mOpsMixin.__truediv__\u001b[1;34m(self, other)\u001b[0m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;129m@unpack_zerodim_and_defer\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__truediv__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    123\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__truediv__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m--> 124\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_arith_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moperator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtruediv\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\frame.py:6940\u001b[0m, in \u001b[0;36mDataFrame._arith_method\u001b[1;34m(self, other, op)\u001b[0m\n\u001b[0;32m   6936\u001b[0m other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39mmaybe_prepare_scalar_for_op(other, (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape[axis],))\n\u001b[0;32m   6938\u001b[0m \u001b[38;5;28mself\u001b[39m, other \u001b[38;5;241m=\u001b[39m ops\u001b[38;5;241m.\u001b[39malign_method_FRAME(\u001b[38;5;28mself\u001b[39m, other, axis, flex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, level\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m-> 6940\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dispatch_frame_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6941\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_construct_result(new_data)\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\frame.py:6979\u001b[0m, in \u001b[0;36mDataFrame._dispatch_frame_op\u001b[1;34m(self, right, func, axis)\u001b[0m\n\u001b[0;32m   6973\u001b[0m     \u001b[38;5;66;03m# TODO: The previous assertion `assert right._indexed_same(self)`\u001b[39;00m\n\u001b[0;32m   6974\u001b[0m     \u001b[38;5;66;03m#  fails in cases with empty columns reached via\u001b[39;00m\n\u001b[0;32m   6975\u001b[0m     \u001b[38;5;66;03m#  _frame_arith_method_with_reindex\u001b[39;00m\n\u001b[0;32m   6976\u001b[0m \n\u001b[0;32m   6977\u001b[0m     \u001b[38;5;66;03m# TODO operate_blockwise expects a manager of the same type\u001b[39;00m\n\u001b[0;32m   6978\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(\u001b[38;5;28mall\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 6979\u001b[0m         bm \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moperate_blockwise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   6980\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# error: Argument 1 to \"operate_blockwise\" of \"ArrayManager\" has\u001b[39;49;00m\n\u001b[0;32m   6981\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# incompatible type \"Union[ArrayManager, BlockManager]\"; expected\u001b[39;49;00m\n\u001b[0;32m   6982\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# \"ArrayManager\"\u001b[39;49;00m\n\u001b[0;32m   6983\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# error: Argument 1 to \"operate_blockwise\" of \"BlockManager\" has\u001b[39;49;00m\n\u001b[0;32m   6984\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# incompatible type \"Union[ArrayManager, BlockManager]\"; expected\u001b[39;49;00m\n\u001b[0;32m   6985\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# \"BlockManager\"\u001b[39;49;00m\n\u001b[0;32m   6986\u001b[0m \u001b[43m            \u001b[49m\u001b[43mright\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[0;32m   6987\u001b[0m \u001b[43m            \u001b[49m\u001b[43marray_op\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   6988\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6989\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(bm)\n\u001b[0;32m   6991\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(right, Series) \u001b[38;5;129;01mand\u001b[39;00m axis \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   6992\u001b[0m     \u001b[38;5;66;03m# axis=1 means we want to operate row-by-row\u001b[39;00m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\internals\\managers.py:1409\u001b[0m, in \u001b[0;36mBlockManager.operate_blockwise\u001b[1;34m(self, other, array_op)\u001b[0m\n\u001b[0;32m   1405\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moperate_blockwise\u001b[39m(\u001b[38;5;28mself\u001b[39m, other: BlockManager, array_op) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BlockManager:\n\u001b[0;32m   1406\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1407\u001b[0m \u001b[38;5;124;03m    Apply array_op blockwise with another (aligned) BlockManager.\u001b[39;00m\n\u001b[0;32m   1408\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1409\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moperate_blockwise\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43marray_op\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\internals\\ops.py:63\u001b[0m, in \u001b[0;36moperate_blockwise\u001b[1;34m(left, right, array_op)\u001b[0m\n\u001b[0;32m     61\u001b[0m res_blks: \u001b[38;5;28mlist\u001b[39m[Block] \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m lvals, rvals, locs, left_ea, right_ea, rblk \u001b[38;5;129;01min\u001b[39;00m _iter_block_pairs(left, right):\n\u001b[1;32m---> 63\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[43marray_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrvals\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     64\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m left_ea \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m right_ea \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(res_values, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreshape\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m     65\u001b[0m         res_values \u001b[38;5;241m=\u001b[39m res_values\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:222\u001b[0m, in \u001b[0;36marithmetic_op\u001b[1;34m(left, right, op)\u001b[0m\n\u001b[0;32m    217\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m# TODO we should handle EAs consistently and move this check before the if/else\u001b[39;00m\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# (https://github.com/pandas-dev/pandas/issues/41165)\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     _bool_arith_check(op, left, right)\n\u001b[1;32m--> 222\u001b[0m     res_values \u001b[38;5;241m=\u001b[39m \u001b[43m_na_arithmetic_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res_values\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:170\u001b[0m, in \u001b[0;36m_na_arithmetic_op\u001b[1;34m(left, right, op, is_cmp)\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m    165\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_cmp \u001b[38;5;129;01mand\u001b[39;00m (is_object_dtype(left\u001b[38;5;241m.\u001b[39mdtype) \u001b[38;5;129;01mor\u001b[39;00m is_object_dtype(right)):\n\u001b[0;32m    166\u001b[0m         \u001b[38;5;66;03m# For object dtype, fallback to a masked operation (only operating\u001b[39;00m\n\u001b[0;32m    167\u001b[0m         \u001b[38;5;66;03m#  on the non-missing values)\u001b[39;00m\n\u001b[0;32m    168\u001b[0m         \u001b[38;5;66;03m# Don't do this for comparisons, as that will handle complex numbers\u001b[39;00m\n\u001b[0;32m    169\u001b[0m         \u001b[38;5;66;03m#  incorrectly, see GH#32047\u001b[39;00m\n\u001b[1;32m--> 170\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43m_masked_arith_op\u001b[49m\u001b[43m(\u001b[49m\u001b[43mleft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mright\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    171\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    172\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[1;32mC:\\veighna_studio\\lib\\site-packages\\pandas\\core\\ops\\array_ops.py:108\u001b[0m, in \u001b[0;36m_masked_arith_op\u001b[1;34m(x, y, op)\u001b[0m\n\u001b[0;32m    106\u001b[0m     \u001b[38;5;66;03m# See GH#5284, GH#5035, GH#19448 for historical reference\u001b[39;00m\n\u001b[0;32m    107\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m--> 108\u001b[0m         result[mask] \u001b[38;5;241m=\u001b[39m \u001b[43mop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mxrav\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myrav\u001b[49m\u001b[43m[\u001b[49m\u001b[43mmask\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    110\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    111\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(y):\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'str'"
     ]
    }
   ],
   "source": [
    "df.pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ma_sma.to_csv('analy_eg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR: Could not find a version that satisfies the requirement upgrade (from versions: none)\n",
      "ERROR: No matching distribution found for upgrade\n"
     ]
    }
   ],
   "source": [
    "!pip install upgrade numpy"
   ]
  }
 ],
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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